CT provides excellent contrast for the cervial spine vertebrae cervical spine, but has poor contrast for intervertebral discs, nerves and muscles. MRI provides excellent contrast for intervertebral discs, nerves and muscles. This projects goal is to register CT and MR images of the cervical spine and provide tools for image visualizing and manipulating. Methods MR images suffer from significant geometric distortion which we correct using our patented algorithm. We segment the CT and MR images to isolate individual vertebra of the cervical spine. Features from CT and MR images are matched to find the rigid body transformations (RBT) between the CT and MR, thus completing registration. We assume a piece-wise RBT between CT and MR images of each vertebra since patient posture is different in CT and MR images. CT-space is then mapped to the MR-space producing a composite image set. We developed a spiral CT and 3D fast GRE MR protocols to highlight the image-features. Automatic and semiautomatic tools to localize vertebrae in CT and MR for matching have been developed. Our SMBR uses contour detection followed by triangulation to generate the surfaces, which are then matched using a nonlinear optimization algorithm. For ICBR, we have developed a set of filters optimized for the imaging protocols to generate feature images. These feature images are then matched using a multi resolution pyramidal search algorithm. We have created a number of tools to visualize the composite images: flood fill, window in window out, 3D surface renderings. Discussion Fusion accuracy of the order of lmm has been achieved in cadaver brain models. Preliminary demonstrations in 9 patients resulted in enthusiastic, positive responses from physicians. The physicians were able to visualize the spinal organization of individual patients significantly better by using our tools compared to the traditional light-box-based mental visualization.